We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone.
Recent articles
- CaMeL offers a promising new direction for mitigating prompt injection attacks - 11th April 2025
- Model Context Protocol has prompt injection security problems - 9th April 2025
- Long context support in LLM 0.24 using fragments and template plugins - 7th April 2025